Convolutional Neural Networks for Sentence Classification
Abstract
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2014
- DOI:
- 10.48550/arXiv.1408.5882
- arXiv:
- arXiv:1408.5882
- Bibcode:
- 2014arXiv1408.5882K
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Neural and Evolutionary Computing
- E-Print:
- To appear in EMNLP 2014